Classification of error rate of data retrieved from memory cells

ABSTRACT

A memory sub-system configured to: measure a plurality of sets of signal and noise characteristics of a group of memory cells in a memory device; determine a plurality of optimized read voltages of the group of memory cells from the plurality of sets of signal and noise characteristics respectively; generate features from the plurality of sets of signal and noise characteristics, including at least one compound feature generated from the plurality of sets of signal and noise characteristics; generate, using the features, a classification of a bit error rate of data retrievable from the group of memory cells; and control an operation to read the group of memory cells based on the classification.

FIELD OF THE TECHNOLOGY

At least some embodiments disclosed herein relate to memory systems in general, and more particularly, but not limited to memory systems having a binary classification decision tree for classification of error rate of data retrievable from memory cells in an integrated circuit memory device.

BACKGROUND

A memory sub-system can include one or more memory devices that store data. The memory devices can be, for example, non-volatile memory devices and volatile memory devices. In general, a host system can utilize a memory sub-system to store data at the memory devices and to retrieve data from the memory devices.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings in which like references indicate similar elements.

FIG. 1 illustrates an example computing system having a memory sub-system in accordance with some embodiments of the present disclosure.

FIG. 2 illustrates an integrated circuit memory device having a calibration circuit configured to measure signal and noise characteristics according to one embodiment.

FIG. 3 shows an example of measuring signal and noise characteristics to improve memory operations according to one embodiment.

FIGS. 4-7 illustrate self adapting iterative read calibration during the execution of a read command according to one embodiment.

FIG. 8 illustrates the generation of compound features for the classification of the error rate of data retrieved from memory cells according to one embodiment.

FIG. 9 illustrates an implementation of a data integrity classifier implemented based on binary classification decision tree according to one embodiment.

FIG. 10 shows a method of classifying the integrity of data retrieved from memory cells using features generated according to one embodiment.

FIG. 11 is a block diagram of an example computer system in which embodiments of the present disclosure can operate.

DETAILED DESCRIPTION

At least some aspects of the present disclosure are directed to a memory sub-system having a data integrity classifier implemented efficiently using a binary classification decision tree technique. A memory sub-system can be a storage device, a memory module, or a hybrid of a storage device and memory module. Examples of storage devices and memory modules are described below in conjunction with FIG. 1. In general, a host system can utilize a memory sub-system that includes one or more components, such as memory devices that store data. The host system can provide data to be stored at the memory sub-system and can request data to be retrieved from the memory sub-system.

An integrated circuit memory cell (e.g., a flash memory cell) can be programmed to store data by the way of its state at a threshold voltage. For example, if the memory cell is configured/programmed in a state that allows a substantial current to pass the memory cell at the threshold voltage, the memory cell is storing a bit of one; and otherwise, the memory cell is storing a bit of zero. Further, a memory cell can store multiple bits of data by being configured/programmed differently at multiple threshold voltages. For example, the memory cell can store multiple bits of data by having a combination of states at the multiple threshold voltages; and different combinations of the states of the memory cell at the threshold voltages can be interpreted to represent different states of bits of data that is stored in the memory cell.

However, after the states of integrated circuit memory cells are configured/programmed using write operations to store data in the memory cells, the optimized threshold voltage for reading the memory cells can shift due to a number of factors, such as charge loss, read disturb, cross-temperature effect (e.g., write and read at different operating temperatures), etc., especially when a memory cell is programmed to store multiple bits of data.

Conventional calibration circuitry has been used to self-calibrate a memory region in applying read level signals to account for shift of threshold voltages of memory cells within the memory region. During the calibration, the calibration circuitry is configured to apply different test signals to the memory region to count the numbers of memory cells that output a specified data state for the test signals. Based on the counts, the calibration circuitry determines a read level offset value as a response to a calibration command.

At least some aspects of the present disclosure address the above and other deficiencies by classifying the bit error rate of data retrievable from memory cells using signal and noise characteristics measured near estimated locations of optimized read voltages of the memory cells and using at least compound features computed from the signal and noise characteristics measured for the multiple optimized read voltages. For example, a data integrity classifier generates a classification of the bit error rate of data retrievable from memory cells based on features calculated from signal and noise characteristics of memory cells measured for multiple read voltages. The features can include compound features that are calculated iteratively or progressively using signal and noise characteristics of memory cells measured for lower read voltages, while signal and noise characteristics of the memory cells are being measured for a higher read voltage. The classification of the bit error rate of the data retrievable from the memory cells can be used to control the operations to read data from the memory cells. The compound features can be computed efficiently using an iterative or progressive technique where the compound features are calculated initially based on signal and noise characteristics measured for lower optimized read voltages while signal and noise characteristics for higher optimized read voltages are being measured or have not yet been measured. The compound features are further updated based on the signal and noise characteristics measured for each higher optimized read voltage when the signal and noise characteristics for the higher optimized read voltage become available. The classification result of the bit error rate can be used to select a processing path in reading data from the memory cells. For example, based on the bit error rate classification, the memory sub-system can decide whether to further calibrate the read voltages, to skip error detection and data recovery, to skip reading the memory cells for soft bit data by applying read voltages that have a predetermined offset from the optimized read voltages, etc.

For example, a memory cell programmed to store multiple bits of data is to be read using multiple read voltages to determine the states of the memory cells at the read voltages and thus the multiple bits stored in the memory cell. The optimized read voltages for reading the multiple states can shift due to data retention effects, such as Quick Charge Loss (QCL), Storage Charge Loss (SCL), etc., and/or other effects. A calibration operation can be performed for each of the read voltages to determine the respective optimized read voltages. During the calibration of each read voltage, a set of signal and noise characteristics of the memory cells can be measured. The multiple set of signal and noise characteristics associated with the multiple optimized read voltages can be used to construct features as input for a predictive model for classifying the bit error rate of data that can be retrieved from the memory cells using the multiple optimized read voltages. Such features can include compound features. A compound feature is based on multiple sets of signal and noise characteristics associated with multiple optimized read voltages respectively.

In some situations, the optimized read voltages can shift over a period of time in a same direction (e.g., towards lower voltages, or towards higher voltages). In general, different optimized read voltages can shift by different amounts, where the higher ones in the optimized read voltages may shift more than the lower ones in the optimized read voltages. A predictive model can be used to predict the shift of a higher optimized read voltage based on the shift(s) of one or more lower optimized read voltages. Thus, once the lower optimized read voltages are determined through calibration, the shift of an optimized read voltage higher than the lower optimized read voltages can be predicted/estimated to correct the initial estimation of the expected location of the higher optimized read voltage. Using the corrected estimation, the calibration for the higher optimized read voltage can be performed to identify an optimized read voltage with improved precision and/or to avoid a failure in calibration.

FIG. 1 illustrates an example computing system 100 that includes a memory sub-system 110 in accordance with some embodiments of the present disclosure. The memory sub-system 110 can include media, such as one or more volatile memory devices (e.g., memory device 140), one or more non-volatile memory devices (e.g., memory device 130), or a combination of such.

A memory sub-system 110 can be a storage device, a memory module, or a hybrid of a storage device and memory module. Examples of a storage device include a solid-state drive (SSD), a flash drive, a universal serial bus (USB) flash drive, an embedded Multi-Media Controller (eMMC) drive, a Universal Flash Storage (UFS) drive, a secure digital (SD) card, and a hard disk drive (HDD). Examples of memory modules include a dual in-line memory module (DIMM), a small outline DIMM (SO-DIMM), and various types of non-volatile dual in-line memory module (NVDIMM).

The computing system 100 can be a computing device such as a desktop computer, laptop computer, network server, mobile device, a vehicle (e.g., airplane, drone, train, automobile, or other conveyance), Internet of Things (IoT) enabled device, embedded computer (e.g., one included in a vehicle, industrial equipment, or a networked commercial device), or such computing device that includes memory and a processing device.

The computing system 100 can include a host system 120 that is coupled to one or more memory sub-systems 110. FIG. 1 illustrates one example of a host system 120 coupled to one memory sub-system 110. As used herein, “coupled to” or “coupled with” generally refers to a connection between components, which can be an indirect communicative connection or direct communicative connection (e.g., without intervening components), whether wired or wireless, including connections such as electrical, optical, magnetic, etc.

The host system 120 can include a processor chipset (e.g., processing device 118) and a software stack executed by the processor chipset. The processor chipset can include one or more cores, one or more caches, a memory controller (e.g., controller 116) (e.g., NVDIMM controller), and a storage protocol controller (e.g., PCIe controller, SATA controller). The host system 120 uses the memory sub-system 110, for example, to write data to the memory sub-system 110 and read data from the memory sub-system 110.

The host system 120 can be coupled to the memory sub-system 110 via a physical host interface. Examples of a physical host interface include, but are not limited to, a serial advanced technology attachment (SATA) interface, a peripheral component interconnect express (PCIe) interface, universal serial bus (USB) interface, Fibre Channel, Serial Attached SCSI (SAS), a double data rate (DDR) memory bus, Small Computer System Interface (SCSI), a dual in-line memory module (DIMM) interface (e.g., DIMM socket interface that supports Double Data Rate (DDR)), Open NAND Flash Interface (ONFI), Double Data Rate (DDR), Low Power Double Data Rate (LPDDR), or any other interface. The physical host interface can be used to transmit data between the host system 120 and the memory sub-system 110. The host system 120 can further utilize an NVM Express (NVMe) interface to access components (e.g., memory devices 130) when the memory sub-system 110 is coupled with the host system 120 by the PCIe interface. The physical host interface can provide an interface for passing control, address, data, and other signals between the memory sub-system 110 and the host system 120. FIG. 1 illustrates a memory sub-system 110 as an example. In general, the host system 120 can access multiple memory sub-systems via a same communication connection, multiple separate communication connections, and/or a combination of communication connections.

The processing device 118 of the host system 120 can be, for example, a microprocessor, a central processing unit (CPU), a processing core of a processor, an execution unit, etc. In some instances, the controller 116 can be referred to as a memory controller, a memory management unit, and/or an initiator. In one example, the controller 116 controls the communications over a bus coupled between the host system 120 and the memory sub-system 110. In general, the controller 116 can send commands or requests to the memory sub-system 110 for desired access to memory devices 130,140. The controller 116 can further include interface circuitry to communicate with the memory sub-system 110. The interface circuitry can convert responses received from memory sub-system 110 into information for the host system 120.

The controller 116 of the host system 120 can communicate with controller 115 of the memory sub-system 110 to perform operations such as reading data, writing data, or erasing data at the memory devices 130,140 and other such operations. In some instances, the controller 116 is integrated within the same package of the processing device 118. In other instances, the controller 116 is separate from the package of the processing device 118. The controller 116 and/or the processing device 118 can include hardware such as one or more integrated circuits (ICs) and/or discrete components, a buffer memory, a cache memory, or a combination thereof. The controller 116 and/or the processing device 118 can be a microcontroller, special purpose logic circuitry (e.g., a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc.), or another suitable processor.

The memory devices 130, 140 can include any combination of the different types of non-volatile memory components and/or volatile memory components. The volatile memory devices (e.g., memory device 140) can be, but are not limited to, random access memory (RAM), such as dynamic random access memory (DRAM) and synchronous dynamic random access memory (SDRAM).

Some examples of non-volatile memory components include a negative-and (or, NOT AND) 1(NAND) type flash memory and write-in-place memory, such as three-dimensional cross-point (“3D cross-point”) memory. A cross-point array of non-volatile memory can perform bit storage based on a change of bulk resistance, in conjunction with a stackable cross-gridded data access array. Additionally, in contrast to many flash-based memories, cross-point non-volatile memory can perform a write in-place operation, where a non-volatile memory cell can be programmed without the non-volatile memory cell being previously erased. NAND type flash memory includes, for example, two-dimensional NAND (2D NAND) and three-dimensional NAND (3D NAND).

Each of the memory devices 130 can include one or more arrays of memory cells. One type of memory cell, for example, single level cells (SLC) can store one bit per cell. Other types of memory cells, such as multi-level cells (MLCs), triple level cells (TLCs), quad-level cells (QLCs), and penta-level cells (PLC) can store multiple bits per cell. In some embodiments, each of the memory devices 130 can include one or more arrays of memory cells such as SLCs, MLCs, TLCs, QLCs, or any combination of such. In some embodiments, a particular memory device can include an SLC portion, and an MLC portion, a TLC portion, or a QLC portion of memory cells. The memory cells of the memory devices 130 can be grouped as pages that can refer to a logical unit of the memory device used to store data. With some types of memory (e.g., NAND), pages can be grouped to form blocks.

Although non-volatile memory devices such as 3D cross-point type and NAND type memory (e.g., 2D NAND, 3D NAND) are described, the memory device 130 can be based on any other type of non-volatile memory, such as read-only memory (ROM), phase change memory (PCM), self-selecting memory, other chalcogenide based memories, ferroelectric transistor random-access memory (FeTRAM), ferroelectric random access memory (FeRAM), magneto random access memory (MRAM), Spin Transfer Torque (STT)-MRAM, conductive bridging RAM (CBRAM), resistive random access memory (RRAM), oxide based RRAM (OxRAM), negative-or (NOR) flash memory, and electrically erasable programmable read-only memory (EEPROM).

A memory sub-system controller 115 (or controller 115 for simplicity) can communicate with the memory devices 130 to perform operations such as reading data, writing data, or erasing data at the memory devices 130 and other such operations (e.g., in response to commands scheduled on a command bus by controller 116). The controller 115 can include hardware such as one or more integrated circuits (ICs) and/or discrete components, a buffer memory, or a combination thereof. The hardware can include digital circuitry with dedicated (i.e., hard-coded) logic to perform the operations described herein. The controller 115 can be a microcontroller, special purpose logic circuitry (e.g., a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc.), or another suitable processor.

The controller 115 can include a processing device 117 (processor) configured to execute instructions stored in a local memory 119. In the illustrated example, the local memory 119 of the controller 115 includes an embedded memory configured to store instructions for performing various processes, operations, logic flows, and routines that control operation of the memory sub-system 110, including handling communications between the memory sub-system 110 and the host system 120.

In some embodiments, the local memory 119 can include memory registers storing memory pointers, fetched data, etc. The local memory 119 can also include read-only memory (ROM) for storing micro-code. While the example memory sub-system 110 in FIG. 1 has been illustrated as including the controller 115, in another embodiment of the present disclosure, a memory sub-system 110 does not include a controller 115, and can instead rely upon external control (e.g., provided by an external host, or by a processor or controller separate from the memory sub-system).

In general, the controller 115 can receive commands or operations from the host system 120 and can convert the commands or operations into instructions or appropriate commands to achieve the desired access to the memory devices 130. The controller 115 can be responsible for other operations such as wear leveling operations, garbage collection operations, error detection and error-correcting code (ECC) operations, encryption operations, caching operations, and address translations between a logical address (e.g., logical block address (LBA), namespace) and a physical address (e.g., physical block address) that are associated with the memory devices 130. The controller 115 can further include host interface circuitry to communicate with the host system 120 via the physical host interface. The host interface circuitry can convert the commands received from the host system into command instructions to access the memory devices 130 as well as convert responses associated with the memory devices 130 into information for the host system 120.

The memory sub-system 110 can also include additional circuitry or components that are not illustrated. In some embodiments, the memory sub-system 110 can include a cache or buffer (e.g., DRAM) and address circuitry (e.g., a row decoder and a column decoder) that can receive an address from the controller 115 and decode the address to access the memory devices 130.

In some embodiments, the memory devices 130 include local media controllers 150 that operate in conjunction with memory sub-system controller 115 to execute operations on one or more memory cells of the memory devices 130. An external controller (e.g., memory sub-system controller 115) can externally manage the memory device 130 (e.g., perform media management operations on the memory device 130). In some embodiments, a memory device 130 is a managed memory device, which is a raw memory device combined with a local controller (e.g., local controller 150) for media management within the same memory device package. An example of a managed memory device is a managed NAND (MNAND) device.

The controller 115 and/or a memory device 130 can include a data integrity classifier 114 that has a feature generator configured to generate compound features as input for the classification of the bit error rate of data retrievable from the memory cells using multiple optimized read voltages. The compound features are generated based on multiple sets of signal and noise characteristics measured during the calibration of the multiple optimized read voltages respectively. In some embodiments, the controller 115 in the memory sub-system 110 includes at least a portion of the data integrity classifier 114. In other embodiments, or in combination, the controller 116 and/or the processing device 118 in the host system 120 includes at least a portion of the data integrity classifier 114. For example, the controller 115, the controller 116, and/or the processing device 118 can include logic circuitry implementing the data integrity classifier 114. For example, the controller 115, or the processing device 118 (processor) of the host system 120, can be configured to execute instructions stored in memory for performing the operations of the data integrity classifier 114 described herein. In some embodiments, the data integrity classifier 114 is implemented in an integrated circuit chip disposed in the memory sub-system 110. In other embodiments, the data integrity classifier 114 can be part of firmware of the memory sub-system 110, an operating system of the host system 120, a device driver, or an application, or any combination therein.

The feature generator of the data integrity classifier 114 can receive multiple sets of signal and noise characteristics measured for multiple optimized read voltages of the memory cells in the memory device 130 and process the signal and noise characteristics to generate compound features for the data integrity classifier 114 of the bit error rate of the data retrievable using the multiple optimized read voltages.

For example, the data integrity classifier 114 can be implemented using a Binary Classification Decision Tree (BCDT) technique, or another decision tree based classification technique. For example, the data integrity classifier 114 can be implemented using an artificial neural network (ANN). The data integrity classifier 114 can be trained using a machine learning technique (e.g., a supervised machine learning technique) to compute a classification of the bit error rate in data retrievable from memory cells using a set of optimized read voltages, based on features constructed using signal and noise characteristics of the memory cells measured during the calibration/determination of the optimized read voltages.

For example, data can be encoded to contain redundant information for storing in memory cells. For example, Error Correction Code (ECC) or Low-Density Parity-Check (LDPC) code can be used to encode data for storing in memory cells. The data retrieved from the memory cells can be decoded in error detection and recovery operations. When the decoding is successful, the bit error rate in the retrieved data can be calculated and/or classified. When the decoding is not successful, the bit error rate is in a category of too high for decoding. A training data set can be generated by computing features from signal and noise characteristics of the memory cells measured during the calibration/determination of optimized read voltages and the bit error rate/classification of the data retrieved using the optimized read voltages, where the bit error rate/classification is calculated from the result of decoding the retrieved data. The training data set can be used to train the data integrity classifier 114 to minimize the differences between the bit error rate/classification predicted by the data integrity classifier 114 using the features and the corresponding the bit error rate/classification calculated from the result of decoding the retrieved data.

For example, the data integrity classifier 114 can be trained to predict whether the retrieved data can decode successfully, and if so the estimated bit error rate of the retrieved data. For example, the memory sub-system 110 can have multiple decoders that have different requirements/inputs and different performance levels in power consumption, error recovery capability, latency, etc. The data integrity classifier 114 can be trained to predict which of the decoders, if any, can successfully decode the retrieved data. After the data integrity classifier 114 is trained, the prediction of the data integrity classifier 114 can be used to select a decoder before attempting to decode.

A compound feature can be constructed as a function of multiple sets of signal and noise characteristics measured for multiple optimized read voltages respectively. An example of the compound feature is a minimum (or a maximum) of a quantity across the multiple sets of signal and noise characteristics. Another example of the compound feature is a minimum (or a maximum) of the sum (or difference) of a quantity in two sets of signal and noise characteristics associated with two adjacent optimized read voltages when the optimized read voltages are sorted in an increasing order.

Optionally, the feature generator 113 can start the computation of the compound feature after receiving the multiple sets of signal and noise characteristics associated with the multiple optimized read voltages respectively.

Preferably, the feature generator 113 can start the computation of the compound feature before the multiple sets of signal and noise characteristics are all available. The feature generator 113 can iteratively or progressively compute the compound feature based on available sets of signal and noise characteristics. When the signal and noise characteristics associated with an optimized read voltage becomes available, the compound feature computed based on signal and noise characteristics of optimized read voltages lower than the optimized read voltage can be updated, while another optimized read voltage is being calibrated to measure its signal and noise characteristics. Thus, the compound feature can be built on the fly as more optimized read voltages are calibrated and their signal and noise characteristics measured. Such an iterative or progressive approach in calculating the compound feature can reduce the resource requirements of the feature generator 113 and/or its latency in providing the computation results relative to the availability of the last set of signal and noise characteristics of the highest optimized read voltage.

FIG. 2 illustrates an integrated circuit memory device 130 having a calibration circuit 145 configured to measure signal and noise characteristics according to one embodiment. For example, the memory devices 130 in the memory sub-system 110 of FIG. 1 can be implemented using the integrated circuit memory device 130 of FIG. 2.

The integrated circuit memory device 130 can be enclosed in a single integrated circuit package. The integrated circuit memory device 130 includes multiple groups 131, . . . , 133 of memory cells that can be formed in one or more integrated circuit dies. A typical memory cell in a group 131, . . . , 133 can be programmed to store one or more bits of data.

Some of the memory cells in the integrated circuit memory device 130 can be configured to be operated together for a particular type of operations. For example, memory cells on an integrated circuit die can be organized in planes, blocks, and pages. A plane contains multiple blocks; a block contains multiple pages; and a page can have multiple strings of memory cells. For example, an integrated circuit die can be the smallest unit that can independently execute commands or report status; identical, concurrent operations can be executed in parallel on multiple planes in an integrated circuit die; a block can be the smallest unit to perform an erase operation; and a page can be the smallest unit to perform a data program operation (to write data into memory cells). Each string has its memory cells connected to a common bitline; and the control gates of the memory cells at the same positions in the strings in a block or page are connected to a common wordline. Control signals can be applied to wordlines and bitlines to address the individual memory cells.

The integrated circuit memory device 130 has a communication interface 147 to receive an address 135 from the controller 115 of a memory sub-system 110 and to provide the data 137 retrieved from the memory address 135. An address decoder 141 of the integrated circuit memory device 130 converts the address 135 into control signals to select the memory cells in the integrated circuit memory device 130; and a read/write circuit 143 of the integrated circuit memory device 130 performs operations to determine data stored in the addressed memory cells or to program the memory cells to have states corresponding to storing the data 137.

The integrated circuit memory device 130 has a calibration circuit 145 configured to determine measurements of signal and noise characteristics 139 of memory cells in a group (e.g., 131, . . . , or 133) and provide the signal and noise characteristics 139 to the controller 115 of a memory sub-system 110 via the communication interface 147.

In at least some embodiments, the calibration circuit 145 also provides, to the controller 115 via the communication interface 147, the signal and noise characteristics 139 measured to determine the read level offset value. In some embodiments, the read level offset value can be used to understand, quantify, or estimate the signal and noise characteristics 139. In other embodiments, the statistics of memory cells in a group or region that has a particular state at one or more test voltages can be provided as the signal and noise characteristics 139.

For example, the calibration circuit 145 can measure the signal and noise characteristics 139 by reading different responses from the memory cells in a group (e.g., 131, . . . , 133) by varying operating parameters used to read the memory cells, such as the voltage(s) applied during an operation to read data from memory cells.

For example, the calibration circuit 145 can measure the signal and noise characteristics 139 on the fly when executing a command to read the data 137 from the address 135. Since the signal and noise characteristics 139 is measured as part of the operation to read the data 137 from the address 135, the signal and noise characteristics 139 can be used in the feature generator 113 with reduced or no penalty on the latency in the execution of the command to read the data 137 from the address 135.

The calibration circuit 145 is configured to calibrate the read voltages of a group of memory cells (e.g., 131 or 133) one after another in the order of ascending read voltages, starting from the lowest optimized read voltage to the highest optimized read voltage. During a calibration/determination of a particular optimized read voltage, the signal and noise characteristics 139 measured for the optimized read voltages lower than the particular optimized read voltage are available to the feature generator 113 to generate/calculate compound features from the available signal and noise characteristics 139. When the calibration circuit 145 completes the calibration of the particular optimized read voltage, its signal and noise characteristics 139 becomes available for the feature generator 113 to update the compound features to include the consideration of the signal and noise characteristics 139 of the particular optimized read voltage. The feature generator 113 can performed the updating of the compound features during the time period in which the calibration circuit 145 calibrates the next optimized read voltage that is higher than the particular optimized read voltage. The updating can be repeated for successive higher optimized read voltages until all optimized read voltages are calibrated, their signal and noise characteristics 139 measured and used to update the compound features.

FIG. 3 shows an example of measuring signal and noise characteristics 139 to improve memory operations according to one embodiment.

In FIG. 3, the calibration circuit 145 applies different read voltages V_(A), V_(B), V_(C), V_(D), and V_(E) to read the states of memory cells in a group (e.g., 131, . . . , or 133). In general, more or less read voltages can be used to generate the signal and noise characteristics 139.

As a result of the different voltages applied during the read operation, a same memory cell in the group (e.g., 131, . . . , or 133) may show different states. Thus, the counts C_(A), C_(B), C_(C), C_(D), and C_(E) of memory cells having a predetermined state at different read voltages V_(A), V_(B), V_(C), V_(D), and V_(E) can be different in general. The predetermined state can be a state of having substantial current passing through the memory cells, or a state of having no substantial current passing through the memory cells. The counts C_(A), C_(B), C_(C), C_(D), and C_(E) can be referred to as bit counts.

The calibration circuit 145 can measure the bit counts by applying the read voltages V_(A), V_(B), V_(C), V_(D), and V_(E) one at a time on the group (e.g., 131, . . . , or 133) of memory cells.

Alternatively, the group (e.g., 131, . . . , or 133) of memory cells can be configured as multiple subgroups; and the calibration circuit 145 can measure the bit counts of the subgroups in parallel by applying the read voltages V_(A), V_(B), V_(C), V_(D), and V_(E). The bit counts of the subgroups are considered as representative of the bit counts in the entire group (e.g., 131, . . . , or 133). Thus, the time duration of obtaining the counts C_(A), C_(B), C_(C), C_(D), and C_(E) can be reduced.

In some embodiments, the bit counts C_(A), C_(B), C_(C), C_(D), and C_(E) are measured during the execution of a command to read the data 137 from the address 135 that is mapped to one or more memory cells in the group (e.g., 131, . . . , or 133). Thus, the controller 115 does not need to send a separate command to request for the signal and noise characteristics 139 that is based on the bit counts C_(A), C_(B), C_(C), C_(D), and C_(E).

The differences between the bit counts of the adjacent voltages are indicative of the errors in reading the states of the memory cells in the group (e.g., 133, . . . , or 133).

For example, the count difference D_(A) is calculated from C_(A)−C_(B), which is an indication of read threshold error introduced by changing the read voltage from V_(A) to V_(B).

Similarly, D_(B)=C_(B)−C_(C); D_(C)=C_(C)−C_(D); and D_(D)=C_(D)−C_(E).

The curve 157, obtained based on the count differences D_(A), D_(B), D_(C), and D_(D), represents the prediction of read threshold error E as a function of the read voltage. From the curve 157 (and/or the count differences), the optimized read voltage V_(O) can be calculated as the point 153 that provides the lowest read threshold error D_(MIN) on the curve 157.

In one embodiment, the calibration circuit 145 computes the optimized read voltage V_(O) and causes the read/write circuit 143 to read the data 137 from the address 135 using the optimized read voltage V_(O).

Alternatively, the calibration circuit 145 can provide, via the communication interface 147 to the controller 115 of the memory sub-system 110, the count differences D_(A), D_(B), D_(C), and D_(D) and/or the optimized read voltage V_(O) calculated by the calibration circuit 145.

FIG. 3 illustrates an example of generating a set of statistical data (e.g., bit counts and/or count differences) for reading at an optimized read voltage V_(O). In general, a group of memory cells can be configured to store more than one bit in a memory cell; and multiple read voltages are used to read the data stored in the memory cells. A set of statistical data can be similarly measured for each of the read voltages to identify the corresponding optimize read voltage, where the test voltages in each set of statistical data are configured in the vicinity of the expected location of the corresponding optimized read voltage. Thus, the signal and noise characteristics 139 measured for a memory cell group (e.g., 131 or 133) can include multiple sets of statistical data measured for the multiple threshold voltages respectively.

For example, the controller 115 can instruct the memory device 130 to perform a read operation by providing an address 135 and at least one read control parameter. For example, the read control parameter can be a read voltage that is suggested, estimated, or predicted by the controller 115.

The memory device 130 can perform the read operation by determining the states of memory cells at the address 135 at a read voltage and provide the data 137 according to the determined states.

During the read operation, the calibration circuit 145 of the memory device 130 generates the signal and noise characteristics 139. The data 137 and the signal and noise characteristics 139 are provided from the memory device 130 to the controller 115 as a response. Alternatively, the processing of the signal and noise characteristics 139 can be performed at least in part using logic circuitry configured in the memory device 130. For example, the processing of the signal and noise characteristics 139 can be implemented partially or entirely using the processing logic configured in the memory device 130. For example, the processing logic can be implemented using Complementary metal-oxide-semiconductor (CMOS) circuitry formed under the array of memory cells on an integrated circuit die of the memory device 130. For example, the processing logic can be formed, within the integrated circuit package of the memory device 130, on a separate integrated circuit die that is connected to the integrated circuit die having the memory cells using Through-Silicon Vias (TSVs) and/or other connection techniques.

The signal and noise characteristics 139 can be determined based at least in part on the read control parameter. For example, when the read control parameter is a suggested read voltage for reading the memory cells at the address 135, the calibration circuit 145 can compute the read voltages V_(A), V_(B), V_(C), V_(D), and V_(E) that are in the vicinity of the suggested read voltage.

The signal and noise characteristics 139 can include the bit counts C_(A), C_(B), C_(C), C_(D), and C_(E). Alternatively, or in combination, the signal and noise characteristics 139 can include the count differences D_(A), D_(B), D_(C), and D_(D).

Optionally, the calibration circuit 145 uses one method to compute an optimized read voltage V_(O) from the count differences D_(A), D_(B), D_(C), and D_(D); and the controller 115 uses another different method to compute the optimized read voltage V_(O) from the signal and noise characteristics 139 and optionally other data that is not available to the calibration circuit 145.

When the calibration circuit 145 can compute the optimized read voltage V_(O) from the count differences D_(A), D_(B), D_(C), and D_(D) generated during the read operation, the signal and noise characteristics can optionally include the optimized read voltage V_(O). Further, the memory device 130 can use the optimized read voltage V_(O) in determining the hard bit data in the data 137 from the memory cells at the address 135. The soft bit data in the data 137 can be obtained by reading the memory cells with read voltages that are a predetermined offset away from the optimized read voltage V_(O). Alternatively, the memory device 130 uses the controller-specified read voltage provided in the read control parameter in reading the data 137.

The controller 115 can be configured with more processing power than the calibration circuit 145 of the integrated circuit memory device 130. Further, the controller 115 can have other signal and noise characteristics applicable to the memory cells in the group (e.g., 133, . . . , or 133). Thus, in general, the controller 115 can compute a more accurate estimation of the optimized read voltage V_(O) (e.g., for a subsequent read operation, or for a retry of the read operation).

In general, it is not necessary for the calibration circuit 145 to provide the signal and noise characteristics 139 in the form of a distribution of bit counts over a set of read voltages, or in the form of a distribution of count differences over a set of read voltages. For example, the calibration circuit 145 can provide the optimized read voltage V_(O) calculated by the calibration circuit 145, as signal and noise characteristics 139.

The calibration circuit 145 can be configured to generate the signal and noise characteristics 139 (e.g., the bit counts, or bit count differences) as a byproduct of a read operation. The generation of the signal and noise characteristics 139 can be implemented in the integrated circuit memory device 130 with little or no impact on the latency of the read operation in comparison with a typical read without the generation of the signal and noise characteristics 139. Thus, the calibration circuit 145 can determine signal and noise characteristics 139 efficiently as a byproduct of performing a read operation according to a command from the controller 115 of the memory sub-system 110.

In general, the calculation of the optimized read voltage V_(O) can be performed within the memory device 130, or by a controller 115 of the memory sub-system 111 that receives the signal and noise characteristics 139 as part of enriched status response from the memory device 130.

In some instances, the calibration circuit 145 of the memory sub-system 110 is configured to use the signal and noise characteristics 139, measured during calibration of one or more lower optimized read voltages of a group of memory cells (e.g., 131 or 133), to identify an estimated location of a higher optimized read voltage and thus improve the calibration operation performed for the higher optimized read voltage.

For example, the calibration circuit 145 can use a predictive model, trained via machine learning or established via an empirical formula, to predict the location of the higher optimized read voltage. The predication can be based on an initial estimation of the location of the higher optimized read voltage, the initial estimation(s) of the location of the one or more lower optimized read voltages, and the calibrated locations of the one or more lower optimized read voltages, where the calibrated locations of the one or more lower optimized read voltages are determined from the signal and noise characteristics 139 measured during the calibration of the one or more lower optimized read voltages. The prediction can be used in the calibration of the higher optimized read voltage, during which further signal and noise characteristics 139 can be measured in the vicinity of the predicted location to identify a calibrated location of the higher optimized read voltage. The result of the calibration of the higher optimized read voltage can be further used in the calibration of even further higher optimized read voltage iteratively.

For example, a controller 115 of the memory sub-system 110 can initially identify the expected/estimated/predicted locations of the multiple optimized read voltages for reading the states of each memory cell in a group for executing a read command. In response to the read command, the memory device 130 starts to calibrate the lowest one of the multiple optimized read voltages first, using the expected/estimated/predicted location of the lowest optimized read voltage initially identified by the controller 115. The calibration results in the identification of an optimized location of the lowest optimized read voltage, which can have an offset or shift from the expected/estimated/predicted location of the lowest optimized read voltage. The offset or shift of the lowest optimized read voltage can be used to predict/estimate the offset or shift of the second lowest optimized read voltage, and thus improve or correct the expected/estimated/predicted location of the second lowest optimized read voltage. The improved or corrected location for the estimation of the second lowest optimized read voltage can be used in its calibration, which results in the identification of an optimized location of the second lowest optimized read voltage. Subsequently, a further higher optimized read voltage of the memory cells can be calibrated using an improved or corrected location determined from its initial estimated identified by the controller 115 and one or more offsets/shifts of one or more optimized read voltages as calibrated from their initial estimations. Thus, the higher optimized read voltages of a memory cell can be iteratively and adaptively calibrated based on the results of the lower optimized read voltages of the memory cell.

FIGS. 4-7 illustrate self adapting iterative read calibration during the execution of a read command according to one embodiment. For example, the self adapting iterative read calibration can be controlled by the controller 115 of the memory sub-system 110 of FIG. 1, and/or by the calibration circuit 145 of an integrated circuit memory device 130 of FIG. 2, using the signal and noise characteristics 139 measured according to FIG. 3.

FIG. 4 illustrates a read threshold error distribution 157 for reading a group of memory cells (e.g., 131 or 133) at various read voltages. The optimized read voltages V_(O1), V_(O2), and V_(O3) have locations corresponding to local minimum points of the read threshold error distribution 157. When the group of memory cells (e.g., 131 or 133) is read at the optimized read voltages V_(O1), V_(O2), and V_(O3) respectively, the errors in the states determined from the read operations are minimized.

FIG. 4 illustrates an example with multiple optimized read voltages V_(O1), V_(O2), and V_(O3) for reading a group of memory cells (e.g., 131 or 133). In general, a group of memory cells (e.g., 131 or 133) can be programmed to be read via more or less optimized read voltages as illustrated in FIG. 4.

The read threshold error distribution 157 can be measured using the technique illustrated in FIG. 3 (e.g., by determining bit count differences of neighboring read voltages).

When the group of memory cells (e.g., 131 or 133) is initially programmed, or recently calibrated, the locations of the optimized read voltages V_(O1), V_(O2), and V_(O3) are known. However, after a period of time, the locations of the optimized read voltages V_(O1), V_(O2), and V_(O3) can shift, e.g., due to Quick Charge Loss (QCL), Storage Charge Loss (SCL), etc.

FIGS. 5-7 illustrate a read threshold error distribution 161 where the locations of the optimized read voltages have shifted on the axis of read voltage. For example, the locations of the optimized read voltages V_(O1), V_(O2), and V_(O3) can shift downwards such that the new location has a voltage smaller than the corresponding prior location. In other examples, the locations of the optimized read voltages V_(O1), V_(O2), and V_(O3) can shift upwards such that the new location has a voltage larger than the corresponding prior location.

The calibration technique of FIG. 3 determines the location of an optimized read voltage (e.g., V_(O)) on the axis of the read voltage by sampling a portion of the read threshold error distribution 157 in the vicinity of an estimated location (e.g., V_(C)) and determine the location of the local minimum point of the sampled read threshold error distribution 157.

To determine locations of the optimized read voltages that have shifted, the previously known locations of the optimized read voltages V_(O1), V_(O2), and V_(O3) can be used as estimated locations (e.g., V_(C)) for the application of the calibration technique of FIG. 3.

FIGS. 5-7 illustrate the estimated locations V_(C1), V_(C2), and V_(C3) of the optimized read voltages V_(O1), V_(O2), and V_(O3) relative to the new read threshold error distribution 161. In some instances, the controller 115 can compute the estimated locations V_(C1), V_(C2), and V_(C3), based on a formula and/or a predictive model, using parameters available to the controller 115.

FIG. 5 illustrates the application of the technique of FIG. 3 to determine the location of the lowest optimized read voltage V_(O1). Test voltages in the range of V_(A1) to V_(E1) are configured in the vicinity of the estimated location V_(C1). The test voltages V_(A1) to V_(E1) can be applied to read the group of memory cells (e.g., 131 or 133) to determine bit counts at the test voltages, and the count differences that are indicative of the magnitude of read threshold errors. The optimized read voltage V_(O1) can be determined at the local minimum of the portion of the read threshold error distribution 161 sampled via the measured bit differences; and the offset or shift V_(S1) from the estimated location V_(C1) to the calibrated location V_(O1) can be used to determine the estimated shift V_(t1) from the estimated location V_(C2) for the next, higher optimized read voltage V_(O2).

For example, the estimated shift V_(t1) can be determined as the same as the measured shift V_(S1) in the lower optimized read voltage V_(O1) from its initial estimation V_(C1). An alternative empirical formula or predictive model can be used to calculate the estimated shift V_(t1) of the higher optimized read voltage V_(O1) from at least the measured shift V_(S1) of the lower optimized read voltage V_(O2).

The estimated shift V_(t1) determines the improved estimation V_(C2U) of the location of the optimized read voltage V_(O2).

FIG. 6 illustrates the application of the technique of FIG. 3 to determine the location of the optimized read voltage V_(O2). After adjusting the estimation from V_(C2) to V_(C2U), test voltages in the range of V_(A2) to V_(E2) are configured in the vicinity of the improved estimation V_(C2U) (instead of relative to V_(C2)). As a result of the improved estimation V_(C2U), the test voltage range from V_(A2) to V_(E2) is better positioned to capture the optimized read voltage V_(O2). The test voltages V_(A2) to V_(E2) can be applied to read the group of memory cells (e.g., 131 or 133) to determine bit counts at the test voltages, and the count differences that are indicative of the magnitude of read threshold errors. The optimized read voltage V_(O1) can be determined at the local minimum of the portion of the read threshold error distribution 161 sampled via the measuring of the bit differences; and the offset or shift V_(S2) from the initial estimated location V_(C2) to the calibrated location V_(O1) can be used in determining the estimated shift V_(t2) from the estimated location V_(C3) for the next, higher optimized read voltage V_(O3).

For example, the estimated shift V_(t2) can be determined as the same as the measured shift V_(S2) in the lower optimized read voltage V_(O1) from its initial estimation V_(C2). Alternatively, the estimated shift V_(t2) can be determined as a function of both the measured shift V_(S2) in the lower optimized read voltage V_(O1) from its initial estimation V_(C2) and the measured shift V_(S1) in the further lower optimized read voltage V_(O1) from its initial estimation V_(C1). An alternative empirical formula or predictive model can be used to calculate the estimated shift V_(t2) of the higher optimized read voltage V_(O3) from at least the measured shift(s) (e.g., V_(S2) and/or V_(S1)) of one or more lower optimized read voltages (e.g., V_(O2) and/or V_(S1)).

The estimated shift V_(t2) provides the improved estimation V_(C3U) of the location of the optimized read voltage V_(O2).

FIG. 7 illustrates the application of the technique of FIG. 3 to determine the location of the optimized read voltage V_(O3). Test voltages in the range of V_(A3) to V_(E3) are configured in the vicinity of the improved estimation V_(C3U). The test voltages V_(A3) to V_(E3) can be applied to read the group of memory cells (e.g., 131 or 133) to determine bit counts at the test voltages, and the count differences that are indicative of the magnitude of read threshold errors. The optimized read voltage V_(O3) can be determined at the local minimum of the portion of the read threshold error distribution 161 sampled via the bit differences.

As illustrated in FIGS. 6 and 7, the improved estimates V_(C2U) and V_(C3U), calculated adaptively and iteratively, allow the calibrations of higher optimized read voltages V₀₁ and V₀₃ to be performed in improved test voltage ranges that are close to the optimized read voltages V₀₁ and V₀₃. If the test voltages were to be constructed using the initial estimations V_(C2) and V_(C3), the test ranges might not capture the optimized read voltages V₀₁ and V₀₃; and calibrations might fail to identify the optimized read voltages V₀₁ and V₀₃, or fail to identify the optimized read voltages V₀₁ and V₀₃ with sufficient accuracy.

FIG. 8 illustrates the generation of compound features for the classification of the error rate of data retrieved from memory cells according to one embodiment. For example, the compound features can be calculated according to FIG. 8 using the signal and noise characteristics of successively higher optimized read voltages, such as the calibrated/calculated read voltages optimized in a way as illustrated in FIGS. 4-7.

In FIG. 8, an ordered list 171 of estimations of optimized read voltages is identified to, or in, an integrated circuit memory device 130. The calibration circuit 145 starts to perform calibration for the lowest read voltage that is to be calibrated using a technique of FIG. 3.

For example, the corresponding estimations in the list 171 can be used to identify a set of test voltages. The bit counts and/or count differences of a group of memory cells (e.g., 131 or 133) can be measured for the set of test voltages, as illustrated in FIG. 3.

Optionally, when one or more lower optimized read voltages have been computed through calibration, the set of test voltages can be identified based on the corresponding estimations in the list 171 and the offsets of the lower optimized read voltages from their calibrated read voltages, in a way as illustrated in FIGS. 4-7.

During the calibration of the lowest read voltage 173, the read/write circuit 143 applies the test voltages to read the group of memory cell 131. A set of signal and noise characteristics 139 is generated from the statistics (e.g., bit counts and count differences) of the states of the memory cells in the group 131 as read using the test voltages.

After the calibration of the lowest read voltage 173 to be calibrated, the calibration circuit 145 can proceed to calibrate the next 177 lowest read voltage 173 to be calibrated.

During the time period of the calibration circuit 145 calibrating the next 177 lowest read voltage 173 to be calibrated, the feature generator 113 uses the signal and noise characteristics 139 measured for the just calibrated read voltage to generate the updated compound features 175 to include the considerations of the signal and noise characteristics 139 that have been obtained so far.

For example, each set of signal and noise characteristics can include the lowest error indicator D_(MIN) 155 of the calculated read voltage V_(O) optimized for reading the group 133 of memory cells. D_(MIN) can be used as a feature associated with the optimized read voltage V_(O). A compound feature can be the minimum (or the maximum) of D_(MIN) of the multiple calibrated/optimized read voltages V_(O) that correspond to the ordered list 171.

When D_(MIN) 155 is calculated for the lowest one in the ordered list 171, the compound feature can take the value of the D_(MIN) 155 of the lowest one in the ordered list 171. When D_(MIN) 155 is calculated for the next lowest one in the ordered list 171, the feature generator 113 can update the compound feature by comparing the existing value of the compound feature and the D_(MIN) 155 calculated for the next lowest one in the ordered list 171. If the existing value of the compound feature is higher than the D_(MIN) 155 calculated for the next lowest one in the ordered list 171, the compound feature is updated to be equal to the D_(MIN) 155 calculated for the next lowest one in the ordered list 171; otherwise, the existing value of the compound feature is not changed in view of the D_(MIN) 155 calculated for the next lowest one in the ordered list 171. After the updating is performed iteratively/progressively for the entire list 171, the compound feature has the value corresponding to the minimum/smallest of D_(MIN) of the corresponding optimized read voltages V_(O).

The maximum/largest of D_(MAX) of the corresponding optimized read voltages V_(O) 151 can be calculated in a similar way as a compound feature.

In some implementations, D_(MIN) can be estimated as the smallest one of the bit differences D_(A) to D_(D).

In another example, the count differences D_(A) to D_(D) measured to calculate the optimized voltages V_(O) 151 can be evaluated to identify an indication of the maximum D_(MAX) of the sampled read threshold error (e.g., the maximum of D_(A) to D_(MAX) can be used as a feature associated with the optimized voltages V_(O). The smallest of D_(MAX) of the optimized voltages can used as a compound feature; and the largest of D_(MAX) of the optimized voltages can used as another compound feature.

In a further example, the range of read threshold error sampled for the optimized voltage V_(O) can be determined as R=D_(MAX)−D_(MIN). Such a range R can be used as a feature associated with the optimized voltage V_(O). The largest of such ranges R of the optimized voltages can used as a compound feature; and the smallest of such ranges R of the optimized voltages can used as another compound feature.

The compound features updated for all of the optimized read voltages and other features corresponding to the signal and noise characteristics 139 can be used in the data integrity classifier 114 to generate a classification of the bit error rate of data retrievable from the group 131 of memory cells using the multiple calibrated/optimized read voltages V_(O) 151.

In general, the data integrity classifier 114 and/or the feature generator 113 can be implemented in the memory device 130 and/or in the controller 115 of the memory sub-system 110. For example, a feature generator 113 can be implemented in the memory device 130 and configured to iteratively or progressively update 175 the compound features using the most recently obtained signal and noise characteristics 139 of an optimized read voltage, before the signal and noise characteristics 139 of the next optimized read voltage become available.

Alternatively, a data integrity classifier 114 and/or a feature generator 113 can be implemented in the controller 150. After the memory device 130 reports the calibration result of lower read voltages (e.g., V_(O1) and V_(O1)) to the controller 150, the feature generator 113 updates 175 the compound features 175 using the signal and noise characteristics 139 included in the calibration result, while the calibration circuit 145 measures the signal and noise characteristics 139 of higher read voltages (e.g., V_(O3)).

A data integrity classifier 114 and/or a feature generator 113 implemented in the controller 115 can use not only the signal and noise characteristics 139 received from the memory device 130 for the data 137 but also other information that may not be available in the memory device 130, such as charge loss, read disturb, cross-temperature effect, program/erase, data retention, etc. The data integrity classifier 114/feature generator 113 implemented in the controller 115 and the data integrity classifier 114/feature generator 113 implemented in the memory device 130 can have different complexity, and/or different levels of accuracy in their predictions. The data integrity classifier 114/feature generator 113 implemented in the controller 115 and the data integrity classifier 114/feature generator 113 implemented in the memory device 130 can communicate with each other to collaboratively control the calibration operations performed by the calibration circuit 145.

The processing logic of the data integrity classifier 114/feature generator 113 can be implemented using Complementary metal-oxide-semiconductor (CMOS) circuitry formed under the array of memory cells on an integrated circuit die of the memory device 130. For example, the processing logic can be formed, within the integrated circuit package of the memory device 130, on a separate integrated circuit die that is connected to the integrated circuit die having the memory cells using Through-Silicon Vias (TSVs) and/or other connection techniques.

FIG. 9 illustrates an implementation of a data integrity classifier implemented based on binary classification decision tree according to one embodiment. For example, the technique of FIG. 9 can be used to implement the data integrity classifier 114 of FIGS. 1, 2, and/or 8.

For example, the data integrity classifier 114 of FIG. 9 has feature registers 181 that are configured to store the value of the features generated by the feature generator 113. The features can include compound features generated iteratively or progressively in a way as illustrated in FIG. 8, while the calibration circuit 145 progresses from calibrating low read voltages to high read voltages.

The data integrity classifier 114 of FIG. 9 can store a set of thresholds 182. For example, the thresholds 182 can be stored in programmable memory and/or registers.

The data integrity classifier 114 of FIG. 9 further includes two sets 165 and 166 of term selection registers. Each of the term selection register sets (e.g., 165 or 166) has multiple registers, identifying features or thresholds to be selected from the feature registers 181 and the stored thresholds 182 for a comparator 189.

The selection logic 183 is controlled by the term selection register sets 185 and 186 to output two terms 187 and 188 as the input for the comparator 189. In general, the term selection register sets 185 and 186 can be programmed to select two features as terms 187 and 188, a feature as the term 187 and a threshold as the term 188, or a threshold as the term 187 and a feature as the term 188.

The comparator 189 is configured to compare the terms 187 and 188 to determine whether a pre-defined relation is satisfied between the terms 187 and 188. For example, the comparator 189 can be configured to determine whether the term A 187 is greater than or equal to the term B 188 (or whether the term A 187 is less than or equal to the term B 188).

The data integrity classifier 114 of FIG. 9 has a leaf path register file 193 configured to store data identifying the connectivity of nodes in the binary classification decision tree of the data integrity classifier 114.

In general, a node in the binary classification decision tree is either a branch node or a leaf node. A leaf node identifies a classification result/decision. A branch node has an associated comparison and two child nodes. The result of the associated comparison determines which of the child nodes is to be selected in the search for a leaf node that provides a classification result/decision.

The data integrity classifier 114 of FIG. 9 has a leaf selection logic 191. After the comparator 189 generates the comparison result of a branch node, the leaf selection logic 191 identifies a child node based on the leaf path register file 193.

If the leaf path register file 193 indicates that the child node is a further branch node, the leaf selection logic 191 determines the registers in the term selection register sets 185 and 186 associated with the child node (i.e., the further branch node), causing the selection logic 183 to select the corresponding terms 187 and 188 of the comparison of the child node.

However, if the leaf path register file 193 indicates that the child node is a leaf node, the leaf selection logic 191 provides the classification result of the leaf node as the decision 195.

For example, the leaf selection logic 191 can be used to initially identify the registers that stores, in the term selection register sets 185 and 186, the identifications of inputs for a top node in the binary classification decision tree. The identifications provided by the term selection register sets 185 and 186 from the registers identified for the top node causes the selection logic 183 to output terms 187 and 188 by selecting from the feature registers 181 and the thresholds 182 according to the identifications. The comparator 189 generates a comparison result of the top node from the terms 187 and 188 received from the selection logic 183. Based on the result of the comparator 189 and the leaf path register file 193, the leaf selection logic 191 identifies the next node and its associated registers in the term selection register sets 185 and 185. The operation of selecting the next node can be repeated until a leaf node is reached. In response to reaching the leaf node, the leaf selection logic 191 provides the classification result pre-associated with the leaf node as the classification decision 195.

The structure of the data integrity classifier 114 of FIG. 9 can minimize/reduce the number of states for tree-based classification, minimize/reduce the logic circuit required to implement a tree-based classifier, and provide flexibility to configure and re-configure the decision tree.

The data integrity classifier 114 of FIG. 9 evaluates the decision tree one node at a time. Thus, the data integrity classifier 114 of FIG. 9 uses one comparator at a time.

In general, the data integrity classifier 114 of FIG. 9 can use a plurality of different types of comparisons in evaluating the decisions of the branch nodes. Since the data integrity classifier 114 uses one comparator at a time, one comparator for each type of comparisons is sufficient for the data integrity classifier 114 of FIG. 9.

FIG. 9 illustrates an implementation of a data integrity classifier 114 using a binary classification decision tree. The technique of FIG. 9 can be extended to other types of decision trees. In general, a branch node in the decision tree can have more than two child nodes; and the selection of the child nodes can be based on more than two terms. Thus, multiple sets of term selection registers can be used to select the respective terms for the decision of a branch node; and a node decision generator can be used to generate the child selection result from the terms selected from the feature registers 181 and the thresholds 182. The child selection result can be used in the leaf selection logic 191 to select the registers of the next branch node based on the leaf path register file 193 and the child selection result, until a leaf node is reached for the decision 195.

In some implementations, the leaf path register file 193 further stores the term identifications of features and/or thresholds to be selected by the selection logic 183 from the feature registers 181 and the pre-defined thresholds 182. To process a branch node, the leaf selection logic 191 provides the term identifications of the branch node and update the term selection register sets 185 and 186 to cause the selection logic 183 to output the terms 187 and 188 for the branch node.

FIG. 10 shows a method of classifying the integrity of data retrieved from memory cells using features generated according to one embodiment. The method of FIG. 10 can be performed by processing logic that can include hardware (e.g., processing device, circuitry, dedicated logic, programmable logic, microcode, hardware of a device, integrated circuit, etc.), software/firmware (e.g., instructions run or executed on a processing device), or a combination thereof. In some embodiments, the method of FIG. 10 is performed at least in part by the controller 115 of FIG. 1, or processing logic in the memory device 130 of FIG. 2. Although shown in a particular sequence or order, unless otherwise specified, the order of the processes can be modified. Thus, the illustrated embodiments should be understood only as examples, and the illustrated processes can be performed in a different order, and some processes can be performed in parallel. Additionally, one or more processes can be omitted in various embodiments. Thus, not all processes are required in every embodiment. Other process flows are possible.

For example, the method of FIG. 10 can be implemented in a computing system of FIG. 1 with a memory device of FIG. 2 and signal noise characteristics illustrated in FIG. 3 and generated in a way as illustrated in FIGS. 4-7. The method of FIG. 10 can optionally use compound features calculated using the technique of FIG. 8 and implemented at least in part using the structure of a data integrity classifier 114 illustrated in FIG. 9.

At block 301, a set of feature registers 181 of a data integrity classifier 114 stores features generated from signal and noise characteristics 139 of a group of memory cells (e.g., 131 or 133) in a memory device 130.

At block 303, a leaf path register file 193 stores data identifying node connectivity in a decision tree of the data integrity classifier 114.

At block 305, a selection logic 183 selects, from the feature registers, at least one feature as input (e.g., 187 and/or 188) to a node decision logic (e.g., 189).

At block 307, the node decision logic (e.g., 189) generates an output based at least in part on the at least one feature selected from the feature registers 181. The output identifies or indicates a selected child node in the decision tree of the data integrity classifier 114.

At block 309, the data integrity classifier 144 determines whether the child node is a leaf node or a branch node.

If the child node is a branch node, at block 311, a leaf selection logic 191 controls further selection from the feature registers for the node decision logic (e.g., 189) in evaluating the child node according to the data stored in the leaf path register file 193. Operations in blocks 305 to 311 can be repeated until reaching a child node that is a leaf node.

If the child node is a leaf node, at block 313, the leaf selection logic 191 provide a classification pre-associated with the leaf node, in response to the node decision logic (e.g., 189) providing an output that selects the leaf node according to the data stored in the leaf path register file 193.

For example, the classification characterizes a bit error rate of data retrievable from the group of memory cells (e.g., 131 or 133) using the read voltages optimized according to the signal and noise characteristics 139 of the group of memory cells (e.g., 131 or 133). The classification can be used to control an operation to read the group of memory cells (e.g., 131 or 133). For example, based on the classification decision 195, the memory device 130 and/or the memory sub-system 110 can decide to further calibrate the read voltages, to skip error detection and recovery operation, to select a decoder from a plurality of decoders available in the memory device 130 and/or the memory sub-system 110 in decoding the read retrieved from the group of memory cells (e.g., 131 or 133) using the optimized read voltages, etc.

Using the data in the leaf path register file 193, the leaf selection logic 191 can control the selection of the features from the feature registers 181 as inputs (e.g., 187 and/or 188) to the node decision logic (e.g., 189).

Optionally, a set of threshold registers is provided in the data integrity classifier 114 to store pre-defined thresholds. Using the data in the leaf path register file 193, the leaf selection logic 191 can also control the selection of the thresholds from the threshold registers (e.g., 182) as input (e.g., 187 or 188) to the node decision logic (e.g., 189).

For example, a plurality of term selection register sets (e.g., 185 and 186) can be used to store locations in the feature registers (e.g., 181) and the threshold registers (e.g., 182). The outputs of the plurality of term selection register sets (e.g., 185 and 186) instructs the selection logic 183 to select, from the feature registers (e.g., 181) and the threshold registers (e.g., 182) respective terms (e.g., 187, 188) as inputs to the node decision logic (e.g., 189). The node decision log (e.g., 189) computes an output based a pre-defined function of the input terms (e.g., 187, 188). The output identifies or indicates a selected child node according to the connectivity specified in the leaf path register file 193.

For example, the connectivity specified in the leaf path register file 193 can correspond to a binary classification decision tree; and the node decision logic includes a comparator 189.

Operations in blocks 305 to 311 can be performed for branch nodes in the decision one at a time until the leaf node is reached in the decision tree.

Optional, the data integrity classifier 113 can include a feature generator 113 that computes compound features based on measured sets signal and noise characteristics for some optimized read voltages, while the calibration circuit 145 is measuring further sets of measured sets signal and noise characteristics other optimized read voltages, as illustrated in FIG. 8.

For example, the calibration circuit 145 can measure multiple sets of signal and noise characteristics to calculate multiple optimized read voltages respectively. The multiple sets of signal and noise characteristics can include first sets of signal and noise characteristics, and a second set of signal and noise characteristics measured after measuring the first sets of signal and noise characteristics. The feature generator 113 calculates a first compound feature from the first sets of signal and noise characteristics, at least in part in parallel with the calibration circuit 145 measuring the second set. The feature generator 113 updates the first compound feature according to the second set of signal and noise characteristics after the second set becomes available. After the multiple sets of signal and noise characteristics are all measured, the first compound feature can be updated and stored into one of the feature registers 181.

A non-transitory computer storage medium can be used to store instructions of the firmware of a memory sub-system (e.g., 113 and/or 114). When the instructions are executed by the controller 115 and/or the processing device 117, the instructions cause the controller 115 and/or the processing device 117 to perform the methods discussed above.

FIG. 11 illustrates an example machine of a computer system 400 within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, can be executed. In some embodiments, the computer system 400 can correspond to a host system (e.g., the host system 120 of FIG. 1) that includes, is coupled to, or utilizes a memory sub-system (e.g., the memory sub-system 110 of FIG. 1) or can be used to perform the operations of a data integrity classifier 114 (e.g., to execute instructions to perform operations corresponding to the data integrity classifier 114 described with reference to FIGS. 1-10). In alternative embodiments, the machine can be connected (e.g., networked) to other machines in a LAN, an intranet, an extranet, and/or the Internet. The machine can operate in the capacity of a server or a client machine in client-server network environment, as a peer machine in a peer-to-peer (or distributed) network environment, or as a server or a client machine in a cloud computing infrastructure or environment.

The machine can be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a server, a network router, a switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

The example computer system 400 includes a processing device 402, a main memory 404 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), static random access memory (SRAM), etc.), and a data storage system 418, which communicate with each other via a bus 430 (which can include multiple buses).

Processing device 402 represents one or more general-purpose processing devices such as a microprocessor, a central processing unit, or the like. More particularly, the processing device can be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets, or processors implementing a combination of instruction sets. Processing device 402 can also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 402 is configured to execute instructions 426 for performing the operations and steps discussed herein. The computer system 400 can further include a network interface device 408 to communicate over the network 420.

The data storage system 418 can include a machine-readable storage medium 424 (also known as a computer-readable medium) on which is stored one or more sets of instructions 426 or software embodying any one or more of the methodologies or functions described herein. The instructions 426 can also reside, completely or at least partially, within the main memory 404 and/or within the processing device 402 during execution thereof by the computer system 400, the main memory 404 and the processing device 402 also constituting machine-readable storage media. The machine-readable storage medium 424, data storage system 418, and/or main memory 404 can correspond to the memory sub-system 110 of FIG. 1.

In one embodiment, the instructions 426 include instructions to implement functionality corresponding to a data integrity classifier 114 (e.g., the data integrity classifier 114 described with reference to FIGS. 1-10). While the machine-readable storage medium 424 is shown in an example embodiment to be a single medium, the term “machine-readable storage medium” should be taken to include a single medium or multiple media that store the one or more sets of instructions. The term “machine-readable storage medium” shall also be taken to include any medium that is capable of storing or encoding a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “machine-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

Some portions of the preceding detailed descriptions have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the ways used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. The present disclosure can refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage systems.

The present disclosure also relates to an apparatus for performing the operations herein. This apparatus can be specially constructed for the intended purposes, or it can include a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program can be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, each coupled to a computer system bus.

The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general purpose systems can be used with programs in accordance with the teachings herein, or it can prove convenient to construct a more specialized apparatus to perform the method. The structure for a variety of these systems will appear as set forth in the description below. In addition, the present disclosure is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages can be used to implement the teachings of the disclosure as described herein.

The present disclosure can be provided as a computer program product, or software, that can include a machine-readable medium having stored thereon instructions, which can be used to program a computer system (or other electronic devices) to perform a process according to the present disclosure. A machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer). In some embodiments, a machine-readable (e.g., computer-readable) medium includes a machine (e.g., a computer) readable storage medium such as a read only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory components, etc.

In this description, various functions and operations are described as being performed by or caused by computer instructions to simplify description. However, those skilled in the art will recognize what is meant by such expressions is that the functions result from execution of the computer instructions by one or more controllers or processors, such as a microprocessor. Alternatively, or in combination, the functions and operations can be implemented using special purpose circuitry, with or without software instructions, such as using Application-Specific Integrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA). Embodiments can be implemented using hardwired circuitry without software instructions, or in combination with software instructions. Thus, the techniques are limited neither to any specific combination of hardware circuitry and software, nor to any particular source for the instructions executed by the data processing system.

In the foregoing specification, embodiments of the disclosure have been described with reference to specific example embodiments thereof. It will be evident that various modifications can be made thereto without departing from the broader spirit and scope of embodiments of the disclosure as set forth in the following claims. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense. 

What is claimed is:
 1. A memory device, comprising: a plurality of groups of memory cells formed on an integrated circuit die; a calibration circuit configured to measure signal and noise characteristics of a group of memory cells to generate features as input to a decision tree; and a data integrity classifier configured to determine a classification of a bit error rate of data retrievable from the group of memory cells by applying the features to the decision tree, wherein the data integrity classifier includes: a set of feature registers configured to store the features; a comparator; a selection logic configured to select at least one feature from the feature registers as input to the comparator; a leaf path register file configured to store data identifying node connectivity in the decision tree; and a leaf selection logic configured to control the selection logic based on an output of the comparator and the data stored in the leaf path register file, and to provide the classification in response to reaching a leaf node in the decision tree; wherein the memory device is configured to control an operation to read the group of memory cells based on the classification.
 2. The memory device of claim 1, wherein the decision tree is a binary classification decision tree.
 3. The memory device of claim 1, wherein the data integrity classifier further includes: a set of threshold registers configured to store pre-defined thresholds; wherein the selection logic is configured to select a threshold from the set of threshold registers as input to the comparator.
 4. The memory device of claim 3, wherein the data integrity classifier further comprises: a plurality of term selection register sets; wherein the leaf selection logic is configured to use the plurality of term selection register sets to control the selection logic in selecting a plurality of terms respectively; and wherein the comparator is configured to generate the output based on the plurality of terms.
 5. The memory device of claim 4, wherein the output of the comparator is based on whether or not a predefined relation is meet by the plurality of terms.
 6. The memory device of claim 5, wherein the data integrity classifier is configured to evaluate the decision tree one node at a time.
 7. The memory device of claim 6, further comprising: an integrated circuit package enclosing the memory device.
 8. The memory device of claim 5, further comprising: a feature generator; and wherein the calibration circuit is configured to measure a plurality of sets of signal and noise characteristics and determine a plurality of read voltages optimized based on the plurality of sets of signal and noise characteristics respectively; wherein the calibration circuit is configured to measure a second set of signal and noise characteristics after measuring first sets of signal and noise characteristics; and wherein the feature generator is configured to: generate a first compound feature from the first sets of signal and noise characteristics, at least in part in parallel with measuring the second set; and update the first compound feature according to the second set of signal and noise characteristics after the second set becomes available.
 9. A method, comprising: selecting, from feature registers, at least one feature as an input, the at least one feature generated from signal and noise characteristics of a group of memory cells in a memory device; generating an output based at least in part on the at least one feature selected from the feature registers, the output identifying a selected child node in a decision tree; controlling further selecting from the feature registers in evaluating the child node according to data identifying node connectivity in the decision tree; and providing a classification pre-associated with a leaf node in response to an output that selects the leaf node according to the data identifying the node connectivity in the decision tree.
 10. The method of claim 9, further comprising: storing, in the feature registers of a data integrity classifier, features generated from the signal and noise characteristics of the group of memory cells in the memory device; and storing, in a leaf path register file, the data identifying the node connectivity in the decision tree of the data integrity classifier; wherein the input is provided to a node decision logic that generates outputs in selecting child nodes in the decision tree; wherein the further selecting is controlled by a leaf selection logic according to the data identifying node connectivity in the decision tree; and wherein the classification characterizes a bit error rate of data retrievable from the group of memory cells.
 11. The method of claim 10, further comprising: controlling an operation to read the group of memory cells based on the classification.
 12. The method of claim 11, wherein the selecting of the at least one feature from the feature registers is under control of the leaf selection logic; and the method further comprises: storing pre-defined thresholds in a set of threshold registers of the data integrity classifier; and selecting, under control of the leaf selection logic, a threshold from the threshold registers as input to the comparator; wherein the output generated by the node decision logic is a function of the threshold selected from the threshold registers and the at least one feature selected from the feature registers.
 13. The method of claim 12, further comprising: storing in a plurality of term selection register sets, each register in the plurality of term selection register sets identifying, for a branch node in the decision tree, a term stored in the threshold registers and the feature registers; wherein the leaf selection logic causes the plurality of term selection register sets to provide, for the branch node, a plurality of outputs that are used to select a plurality of terms respectively from the threshold registers and the feature registers; and wherein the plurality of terms are provided as input to the node decision logic to generate an output to the leaf selection logic.
 14. The method of claim 13, wherein the decision tree is a binary classification decision tree; and the node decision logic includes a comparator.
 15. The method of claim 13, wherein branch nodes in the decision are evaluated one at a time until the leaf node is reached in the decision tree.
 16. The method of claim 15, further comprising: measuring a plurality of sets of signal and noise characteristics, including first sets of signal and noise characteristics, and a second set of signal and noise characteristics measured after measuring the first sets of signal and noise characteristics; calculating a plurality of read voltages optimized based on the plurality of sets of signal and noise characteristics respectively; generating a first compound feature from the first sets of signal and noise characteristics, at least in part in parallel with measuring the second set; and updating the first compound feature according to the second set of signal and noise characteristics after the second set becomes available, wherein at least one feature stored in the feature registers is based on the first compound feature.
 17. A memory sub-system, comprising: a processing device; and at least one memory device, the memory device having: a group of memory cells formed on an integrated circuit die; and a calibration circuit configured to measure a plurality of sets of signal and noise characteristics of the group of memory cells and determine a plurality of optimized read voltages of the group of memory cells from the plurality of sets of signal and noise characteristics respectively; wherein the memory sub-system includes a data integrity classifier configured to determine a classification of a bit error rate of data retrievable from the group of memory cells, wherein the data integrity classifier includes: feature registers configured to store features calculated from the plurality of sets of signal and noise characteristics of the group of memory cells; threshold registers configured to store pre-defined thresholds; a selection logic configured to select a plurality of terms from the feature registers and the threshold registers; a decision logic configured to generate an output from the plurality of terms, the output identifying a selection of a child node in the decision tree by applying the plurality of terms on a branch node; and a leaf selection logic configured to control selection of terms by the selection logic from the feature registers and the threshold registers, based on the output of the decision logic and data identifying node connectivity in the decision tree; wherein in response to reaching a leaf node in the decision tree, the leaf selection logic is configured to provide a classification of a bit error rate of data retrievable from the group of memory cell, the classification being pre-associated with the leaf node; and wherein the memory sub-system is configured to control an operation to read the group of memory cells based on the classification.
 18. The memory sub-system of claim 17, wherein the data integrity classifier evaluates the decision tree one at a time until reaching the leaf node.
 19. The memory sub-system of claim 18, wherein the decision tree is a binary classification decision tree.
 20. The memory sub-system of claim 19, wherein the decision logic includes a comparator. 